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Segmenting Transparent Objects in the Wild with Transformer
Open accessAbstract
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures. Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset. We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg.
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“In addition, our PVT-Small+Trans2Seg performs better than ResNet50-d16+Trans2Seg (mIoU: 42.6 vs . 39.7, GFlops: 31.6 vs . 79.3).”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [72]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
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Research works printed but not linked: 2
- Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. Convolu- tional sequence to sequence learning. arXiv, 2017.
- Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al. A survey on visual transformer. arXiv, 2020.
Cited by
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Self-citationSegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090
Self-citationSegment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Ji, W., Li, J., Bi, Q., Liu, T., Li, W., & Cheng, L. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1385-0 · Cited by 284
Jia, M., Cheng, X., Lu, S., & Zhang, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3141267 · Cited by 225
A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154
Fang, H., Fang, H.-S., Xu, S., & Lu, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3183256 · Cited by 139
Glass Segmentation using Intensity and Spectral Polarization Cues
Mei, H., Dong, B., Dong, W., Yang, J., Baek, S., Heide, F., Peers, P., Wei, X., & Yang, X. S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01229 · Cited by 116
Park, K.-B., & Lee, J. Y. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac018 · Cited by 109
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent ObjectsPreprint
Ichnowski, J., Avigal, Y., Kerr, J. H., & Goldberg, K. Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14217 · Cited by 114
Zhang, J., Yang, K., Constantinescu, A., Peng, K., Müller, K., & Stiefelhagen, R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3161141 · Cited by 98
Zhang, J., Yang, K., Constantinescu, A., Peng, K., Müller, K., & Stiefelhagen, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00202 · Cited by 88
Visual–Tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Li, S., Yu, H., Ding, W., Liu, H., Ye, L., Xia, C., Wang, X., & Zhang, X.-P. S. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3286071 · Cited by 75
Robotic Perception of Transparent Objects: A Review
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Sun, Z., Zhou, W., Ding, C., & Xia, M. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11030165 · Cited by 64
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Wang, Y., Qi, G., Li, S., Chai, Y., & Li, H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3207893 · Cited by 43
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Shi, H., Hayat, M., & Cai, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00298 · Cited by 42
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Durner, M., Boerdijk, W., Sundermeyer, M., Friedl, W., Márton, Z.-C., & Triebel, R. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636281 · Cited by 40
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Gao, R. Q., Li, M., Yang, S.-J., & Cho, K. (2022) · Remote Sensing · doi:10.3390/rs14030577 · Cited by 40
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Kolbeinsson, B., & Mikolajczyk, K. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00825 · Cited by 34
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Zhang, Y., Wang, Y., Li, H., & Li, S. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548224 · Cited by 32
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Jiang, Y., Zhang, Z., Qin, S., Guo, Y., Li, Z., & Cui, S. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26351-4_2 · Cited by 31
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Zhao, X., Pang, Y., Zhang, L., Lu, H., & Lei Zhang, A. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02058-y · Cited by 29
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Liu, Z., He, Q., Wang, L., Fang, X., & Tang, B. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3281465 · Cited by 27
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METrans: Multi‐encoder transformer for ischemic stroke segmentation
Wang, J., Wang, S., & Liang, W. (2022) · Electronics Letters · doi:10.1049/ell2.12444 · Cited by 23
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Yu, H., Li, S., Liu, H., Xia, C., Ding, W., & Liang, B. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3268041 · Cited by 21
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Xu, H., Tang, X., Ai, B., Yang, F., Wen, Z., & Yang, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3183144 · Cited by 17
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Shi, P., Chen, X., Qi, H., Zhang, C., & Liu, Z.-Q. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/4228610 · Cited by 15
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Junger, C., Speck, H., Landmann, M., Srokos, K., & Notni, G. (2023) · Sensors · doi:10.3390/s23208567 · Cited by 3
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Ni, K., Yang, X., Chen, T., & Zhu, M. (2026) · Robotics · doi:10.3390/robotics15070135 · Cited by 2
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Cross-scale sampling transformer for semantic image segmentation
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Liu, Z., Mu, X., Dong, S., Lu, Y., & Jiang, M. (2022) · Symmetry · doi:10.3390/sym14071454
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Knauthe, V., Weitz, P., Pöllabauer, T., Wirth, T., Rak, A., Kuijper, A., & Fellner, D. W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_17
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Zhang, F., Wu, P., Hu, C., An, W., & Liang, X. (2025) · The Visual Computer · doi:10.1007/s00371-025-03862-4
Bajaj, C., Nguyen, M., & Bhardwaj, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6975-2_17
Metadata
- Authors
- Xie, E., Wang, W., Wang, W., Sun, P., Xu, H., Liang, D., & Luo, P.
- Affiliations
- University of Hong Kong (HK)
- Sensetime (China) (CN)
- Huawei Technologies (China) (CN)
- Venue
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
- Published
- 2021-08
- Pages
- 1194-1200
- OpenAlex
- W3189898414
- Cited by
- 111
- References
- 26
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 31 · 2023: 23 · 2022: 23 · 2021: 10
- FWCI
- 7.11
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- semantic segmentation, Transformer encoder, dictionary lookup, global receptive field, fine-grained segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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